Apache Arrow for Data Ingestion

Moving data is CPU intensive. Serialization and deserialization (SerDe) often consume upwards of 80% of the CPU cycles in a traditional data pipeline.

JSON is flexible but slow. CSV is compact but untyped. Parquet is great for storage but expensive to write row-by-row.

Enter Apache Arrow

Apache Arrow provides a cross-language, columnar memory format. It is the “lingua franca” of modern data analytics.

Zero-Copy Data Sharing

The superpower of Arrow is Zero-Copy.

If we read a Parquet file from S3 into Arrow format in memory using Python (via PyArrow), we can pass that memory pointer directly to a C++ or Rust process to perform a heavy aggregation—without copying the data.

Our Pipeline Architecture

1. Ingest: Connectors read source data and convert immediately to Arrow RecordBatches.
2. Process: Transforms operate on these columnar batches. SIMD instructions can optimize operations across the entire column.
3. Flush: We write batches out to Parquet or the destination sink.

By standardizing on Arrow, Ettaflow ensures that your data moves through our Platform with the absolute minimum amount of CPU overhead, resulting in faster syncs and lower costs.

This efficiency pairs perfectly with our Golang-based orchestration engine, ensuring both compute and memory are optimized suitable for high-throughput environments.